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Model: jinrui123/llamasrnn-grpo-epoch001-merged Source: Original Platform
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*.7z filter=lfs diff=lfs merge=lfs -text
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README.md
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README.md
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---
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license: other
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language:
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- en
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library_name: transformers
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tags:
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- llama
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- llama-3
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- causal-lm
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- clinical
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- grpo
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- lora
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- merged-adapter
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- transformers
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base_model: meta-llama/Llama-3.2-3B-Instruct
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pipeline_tag: text-generation
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---
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# LLMasRNN GRPO Policy Epoch 001 Merged
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This repository contains a merged causal language model produced by taking a LoRA adapter trained in the `LLMasRNN` project and merging it into the base model `meta-llama/Llama-3.2-3B-Instruct`.
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This is the merged checkpoint for:
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- Project: `LLMasRNN`
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- Training stage: `GRPO Phase 1`
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- Epoch artifact: `training/artifacts/epoch_001`
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- Base model: `meta-llama/Llama-3.2-3B-Instruct`
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- Output repo type: merged full weights
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## What This Model Is
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The policy model is intended for longitudinal clinical prediction workflows in the `LLMasRNN` project. In this training phase, the model is optimized as a memory-update / policy head using GRPO-style reinforcement learning over trajectory rollouts and rubric-based rewards.
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The uploaded weights in this repository are not just the adapter. They are the full merged model weights created from:
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- Base model weights from `meta-llama/Llama-3.2-3B-Instruct`
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- A LoRA adapter saved at `training/artifacts/epoch_001/policy_lora`
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## Training Summary
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The training run used a custom GRPO training loop implemented in `training/train/policy_trainer.py` and configured by `training/configs/grpo_phase1.yaml`.
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High-level pipeline for one epoch:
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1. Collect RLN trajectories over the training split.
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2. Sample rollout candidates from the policy.
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3. Score candidates with a fixed rubric judge.
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4. Train the LoRA policy with a custom GRPO loss.
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This merged checkpoint corresponds to the first completed epoch.
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## Base Model
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- Base model: `meta-llama/Llama-3.2-3B-Instruct`
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- Architecture: causal LM
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- Total parameters after merge: `3,221,924,864`
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## LoRA Configuration
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The policy was trained as a LoRA adapter before merge.
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- PEFT type: `LoRA`
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- Task type: `CAUSAL_LM`
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- Rank `r`: `16`
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- LoRA alpha: `32`
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- LoRA dropout: `0.05`
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- Target modules:
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- `q_proj`
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- `k_proj`
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- `v_proj`
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- `o_proj`
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- Trainable parameters before merge: `9,175,040`
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- Trainable fraction before merge: `0.2848%`
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## GRPO / RL Training Configuration
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From `training/configs/grpo_phase1.yaml`:
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- Framework: custom GRPO implementation
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- KL coefficient: `0.05`
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- Clip range: `0.2`
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- Importance clip: `5.0`
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- Inner steps per epoch: `4`
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- Batch size: `4` trajectory groups per update step
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- Rollouts per state / group size `G`: `8`
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- Learning rate: `5e-6`
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- Gradient clipping: `1.0`
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- Warmup steps: `50`
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## Sampling and Reward Setup
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### Policy rollout sampling
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- Temperature: `0.8`
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- Top-p: `0.95`
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- Max new tokens: `512`
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### Predictor model
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- Model: `meta-llama/Llama-3.2-3B-Instruct`
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- Backend: `vLLM`
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- Max model length: `8192`
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### RLN judge
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- Model: `jinrui123/sft_llama3.2_3b_merged`
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- Backend: `vLLM`
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- Max model length: `8192`
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- Temperature: `0.3`
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- Max tokens: `1536`
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### Rubric judge
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- Model path during training: `/data/jf44684/TrainingDataParepation/models/RubricARM-8B-Judge`
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- Backend: `vLLM`
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- Temperature: `0.3`
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- Max tokens: `1536`
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### Reward composition
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- Downstream accuracy bonus enabled: `true`
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- Epsilon accuracy bonus: `0.2`
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## Data / Run Size
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From the saved epoch metadata:
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- Training split: `data/splits/cleaned_df_train_100.json`
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- Validation split: `data/splits/cleaned_df_val_100.json`
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- Number of collected trajectory steps: `390`
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- Number of scored rollout groups: `390`
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- Debug mode: `false`
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## Epoch 001 Metrics
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Saved in `training/artifacts/epoch_001/meta.json`:
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- Loss: `0.436767578125`
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- Policy loss: `0.435302734375`
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- KL: `0.0322265625`
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- Mean absolute advantage: `0.82421875`
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- Mean ratio: `0.8505859375`
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- Mean reward: `0.7509765625`
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|
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These are training-time epoch averages over the 4 inner GRPO update steps for epoch 1.
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## Merge Details
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|
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This repository was created by:
|
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|
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1. Loading the base model `meta-llama/Llama-3.2-3B-Instruct`
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2. Loading the saved LoRA adapter from `training/artifacts/epoch_001/policy_lora`
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3. Calling `merge_and_unload()` with PEFT
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4. Saving the resulting merged full model weights
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The original adapter checkpoint remains the more storage-efficient representation for continued adapter-based training.
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## Intended Use
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||||
|
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This checkpoint is intended for research and experimentation within the `LLMasRNN` project setting:
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|
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- longitudinal clinical prediction
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- diagnosis prediction conditioned on evolving patient summaries
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- RL / GRPO policy experiments
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- ablation or evaluation of merged policy checkpoints
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It is not validated for clinical deployment, medical decision support in production, or unsupervised real-world medical use.
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## Limitations
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- This is an epoch-1 checkpoint, not a converged final model.
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- The training objective is project-specific and depends on custom reward shaping and judge models.
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- Training and evaluation were performed on internal project data/configuration rather than a standardized public benchmark release.
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- Merged weights inherit the usage constraints and limitations of the base Llama model.
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## Loading
|
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Example with Transformers:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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repo_id = "jinrui123/llamasrnn-grpo-epoch001-merged"
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tokenizer = AutoTokenizer.from_pretrained(repo_id)
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model = AutoModelForCausalLM.from_pretrained(repo_id)
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```
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## Notes on Licensing
|
||||
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This repository contains merged weights derived from `meta-llama/Llama-3.2-3B-Instruct`. Use and redistribution must comply with the license and access terms of the original base model.
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93
chat_template.jinja
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chat_template.jinja
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{{- bos_token }}
|
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{%- if custom_tools is defined %}
|
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{%- set tools = custom_tools %}
|
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{%- endif %}
|
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{%- if not tools_in_user_message is defined %}
|
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{%- set tools_in_user_message = true %}
|
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{%- endif %}
|
||||
{%- if not date_string is defined %}
|
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{%- if strftime_now is defined %}
|
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{%- set date_string = strftime_now("%d %b %Y") %}
|
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{%- else %}
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{%- set date_string = "26 Jul 2024" %}
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{%- endif %}
|
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{%- endif %}
|
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{%- if not tools is defined %}
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{%- set tools = none %}
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{%- endif %}
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|
||||
{#- This block extracts the system message, so we can slot it into the right place. #}
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{%- if messages[0]['role'] == 'system' %}
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{%- set system_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{%- set system_message = "" %}
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{%- endif %}
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{#- System message #}
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{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
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{%- if tools is not none %}
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{{- "Environment: ipython\n" }}
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{%- endif %}
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{{- "Cutting Knowledge Date: December 2023\n" }}
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{{- "Today Date: " + date_string + "\n\n" }}
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{%- if tools is not none and not tools_in_user_message %}
|
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{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
|
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
|
||||
{{- "Do not use variables.\n\n" }}
|
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{%- for t in tools %}
|
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{{- t | tojson(indent=4) }}
|
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{{- "\n\n" }}
|
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{%- endfor %}
|
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{%- endif %}
|
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{{- system_message }}
|
||||
{{- "<|eot_id|>" }}
|
||||
|
||||
{#- Custom tools are passed in a user message with some extra guidance #}
|
||||
{%- if tools_in_user_message and not tools is none %}
|
||||
{#- Extract the first user message so we can plug it in here #}
|
||||
{%- if messages | length != 0 %}
|
||||
{%- set first_user_message = messages[0]['content']|trim %}
|
||||
{%- set messages = messages[1:] %}
|
||||
{%- else %}
|
||||
{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
|
||||
{%- endif %}
|
||||
{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
|
||||
{{- "Given the following functions, please respond with a JSON for a function call " }}
|
||||
{{- "with its proper arguments that best answers the given prompt.\n\n" }}
|
||||
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
|
||||
{{- "Do not use variables.\n\n" }}
|
||||
{%- for t in tools %}
|
||||
{{- t | tojson(indent=4) }}
|
||||
{{- "\n\n" }}
|
||||
{%- endfor %}
|
||||
{{- first_user_message + "<|eot_id|>"}}
|
||||
{%- endif %}
|
||||
|
||||
{%- for message in messages %}
|
||||
{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
|
||||
{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
|
||||
{%- elif 'tool_calls' in message %}
|
||||
{%- if not message.tool_calls|length == 1 %}
|
||||
{{- raise_exception("This model only supports single tool-calls at once!") }}
|
||||
{%- endif %}
|
||||
{%- set tool_call = message.tool_calls[0].function %}
|
||||
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
|
||||
{{- '{"name": "' + tool_call.name + '", ' }}
|
||||
{{- '"parameters": ' }}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{{- "}" }}
|
||||
{{- "<|eot_id|>" }}
|
||||
{%- elif message.role == "tool" or message.role == "ipython" %}
|
||||
{{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
|
||||
{%- if message.content is mapping or message.content is iterable %}
|
||||
{{- message.content | tojson }}
|
||||
{%- else %}
|
||||
{{- message.content }}
|
||||
{%- endif %}
|
||||
{{- "<|eot_id|>" }}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
|
||||
{%- endif %}
|
||||
39
config.json
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config.json
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{
|
||||
"architectures": [
|
||||
"LlamaForCausalLM"
|
||||
],
|
||||
"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 128000,
|
||||
"dtype": "bfloat16",
|
||||
"eos_token_id": [
|
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128001,
|
||||
128008,
|
||||
128009
|
||||
],
|
||||
"head_dim": 128,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 3072,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 8192,
|
||||
"max_position_embeddings": 131072,
|
||||
"mlp_bias": false,
|
||||
"model_type": "llama",
|
||||
"num_attention_heads": 24,
|
||||
"num_hidden_layers": 28,
|
||||
"num_key_value_heads": 8,
|
||||
"pretraining_tp": 1,
|
||||
"rms_norm_eps": 1e-05,
|
||||
"rope_scaling": {
|
||||
"factor": 32.0,
|
||||
"high_freq_factor": 4.0,
|
||||
"low_freq_factor": 1.0,
|
||||
"original_max_position_embeddings": 8192,
|
||||
"rope_type": "llama3"
|
||||
},
|
||||
"rope_theta": 500000.0,
|
||||
"tie_word_embeddings": true,
|
||||
"transformers_version": "4.57.1",
|
||||
"use_cache": true,
|
||||
"vocab_size": 128256
|
||||
}
|
||||
12
generation_config.json
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generation_config.json
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|
||||
{
|
||||
"bos_token_id": 128000,
|
||||
"do_sample": true,
|
||||
"eos_token_id": [
|
||||
128001,
|
||||
128008,
|
||||
128009
|
||||
],
|
||||
"temperature": 0.6,
|
||||
"top_p": 0.9,
|
||||
"transformers_version": "4.57.1"
|
||||
}
|
||||
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model-00001-of-00002.safetensors
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model-00001-of-00002.safetensors
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version https://git-lfs.github.com/spec/v1
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size 4965799096
|
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model-00002-of-00002.safetensors
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3
model-00002-of-00002.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:cf0224bb48e79c0fe68e705d75b75c37d3a9b652eb4c25b72070b913590d9fdb
|
||||
size 1459729952
|
||||
262
model.safetensors.index.json
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262
model.safetensors.index.json
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{
|
||||
"metadata": {
|
||||
"total_parameters": 3212749824,
|
||||
"total_size": 6425499648
|
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},
|
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"weight_map": {
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"model.layers.0.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
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"model.layers.0.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
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"model.layers.0.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
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"model.layers.0.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
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"model.layers.0.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
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"model.layers.0.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
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"model.layers.0.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
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"model.layers.1.input_layernorm.weight": "model-00001-of-00002.safetensors",
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"model.layers.1.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
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"model.layers.1.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
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||||
}
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||||
23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|eot_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|eot_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:6b9e4e7fb171f92fd137b777cc2714bf87d11576700a1dcd7a399e7bbe39537b
|
||||
size 17209920
|
||||
2063
tokenizer_config.json
Normal file
2063
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user